Phosphodiesterase (PDE) 7 is a high affinity cAMP-specific PDE whose functional role in T-cells has been the subject of some controversy. Recent findings on tissue distribution, however, support the hypothesis that PDE7 could be a good target for the treatment of airway diseases, T-cell related diseases or central nervous system (CNS) disorders. Therefore, the identification of selective inhibitors targeted against PDE7 enzyme has become an attractive area of research. We report here the first use of the descriptors generated by the CODES program for ligand-based virtual screening. This program codifies molecules from a topological point of view and the generated descriptors are related to the chemical nature of the atoms, the atomic bonds and the connectivity with the rest of the molecule. They are also able to distinguish among stereoisomers. By using this approach, 173 compounds were codified, and their similarity with the reference compound was analysed. Based on the analysis, new potential PDE7 inhibitors have been identified, synthesized and biologically evaluated confirming that CODES descriptors are valid for ligand-based virtual screening and provided new lead compounds for further optimization as potent and selective PDE7 inhibitors.
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http://dx.doi.org/10.1016/j.ejmech.2007.10.027 | DOI Listing |
Expert Opin Drug Discov
January 2025
Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY, USA.
Introduction: Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.
View Article and Find Full Text PDFPharmaceuticals (Basel)
January 2025
Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile.
Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.
View Article and Find Full Text PDFCurr Drug Discov Technol
December 2024
Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences and Research University, PushpViharSector-3, M-B Road, New Delhi, 110017, India.
Background: Computer-Aided Drug Design (CADD) approaches are essential in the drug discovery and development process. Both academic institutions and pharmaceutical and biotechnology corporations utilize them to enhance the efficacy of bioactive compounds.
Objective: This study aims to entice researchers by investigating the benefits of Computer-Aided Drug and Design (CADD) and its fundamental principles.
Mol Ther Oncol
December 2024
Drug Repurposing and Medicines Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia.
Drug repurposing has potential to improve outcomes for high-grade serous ovarian cancer (HGSOC). Repurposing drugs with PARP family binding activity may produce cytotoxic effects through the multiple mechanisms of PARP including DNA repair, cell-cycle regulation, and apoptosis. The aim of this study was to determine existing drugs that have PARP family binding activity and can be repurposed for treatment of HGSOC.
View Article and Find Full Text PDFMolecules
December 2024
Structural Bioinformatics Laboratory, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland.
Transient receptor potential vanilloid (TRPV) 4 is involved in signaling pathways specifically mediating pain and inflammation, making it a promising target for the treatment of various painful and inflammatory conditions. However, only one drug candidate targeting TRPV4 has entered the clinical trials. To identify potential TRPV4 inhibitors for drug development, we screened a library of ion channel-modulating compounds using both structure- and ligand-based virtual screening approaches.
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